Triple
T799281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Sadler |
E17091
|
entity |
| Predicate | spouseNotableFor |
P19181
|
FINISHED |
| Object | benefactor of Harvard College |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: benefactor of Harvard College | Statement: [Ann Sadler, spouseNotableFor, benefactor of Harvard College]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseNotableFor Context triple: [Ann Sadler, spouseNotableFor, benefactor of Harvard College]
-
A.
spouse
Indicates that two entities are married to each other in a legally or socially recognized partnership.
-
B.
firstHolderSpouseOf
Indicates that the first holder in the relation is the spouse (married partner) of the other holder.
-
C.
spouseFamily
Indicates a family relationship formed through marriage, such as between a person and their spouse’s relatives.
-
D.
marriedToUSPresident
Indicates being legally married to an individual who holds or has held the office of President of the United States.
-
E.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7cb26dc8190bdd3a278b8695873 |
completed | March 1, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69a4a5122a008190b0c621b7bc588d41 |
completed | March 1, 2026, 8:44 p.m. |
| PDg | Predicate description generation | batch_69a4a5bed20c81909ecc28bf42594e72 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:38 p.m.